Road-side dust samples were collected during August in 2020 from selected areas of, Al-Rusafa, Baghdad, Iraq. A sedimentological and mineralogical analysis of street dust was conducted. Three areas were selected to study street dusts which are Al-Baladitat, Al-Obaidi and Ziona. The laboratory analyses were done in the Department of Geology, College of Science, University of Baghdad. The heavy metal contents were determined in the roadside dust using XRF Method. It was found that the dust is of muddy texture, and is believed to be transmitted with the various storms blowing on Baghdad or by the wheels of Cars. The results of mineralogical investigation revealed that the dust samples composed of quartz, feldspar, calcite, gypsum and sedimentary rocks fragments, as light minerals and the heavy minerals were found as garnet, epidote, chlorites, pyroxene, biotite, zircon, muscovite, hornblende, tourmaline. The heavy metals (Fe, Co, Ni, Cu, Zn and Pb) in these streets dust samples were studied and used as indicator for pollution. two o main indices are applied: contamination factor, and pollution load index. The contamination factor for Co, Zn, Pb, and Ni is classified as class 2, which indicate moderately contamination, while the contamination factor for Fe and Cu is classified as class 1, which indicate low contamination. The pollution load index values in the all of studied sites are classified as class 2 (Deterioration on site quality) indicating local pollution, as well as denote perfection with class 0 of no pollution.
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The purchase of a home and access to housing is one of the most important requirements for the life of the individual and the stability of living and the development of the prices of houses in general and in Baghdad in particular affected by several factors, including the basic area of the house, the age of the house, the neighborhood in which the housing is available and the basic services, Where the statistical model SSM model was used to model house prices over a period of time from 2000 to 2018 and forecast until 2025 The research is concerned with enhancing the importance of this model and describing it as a standard and important compared to the models used in the analysis of time series after obtaining the
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Was conducted to compare the two methods above and it became clear by comparing the logistic regression model best of a Linear Discriminant function written
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